在家庭层面使用回归方法和机器学习方法预测青少年的非自杀性自我伤害
Si Chen Zhou1, Zhaohe Zhou2, Qi Tang1
1Center for Wise Information Technology of Mental Health Nursing Research, School of Nursing, Wuhan University, Wuhan, China.
Journal of affective disorders
|February 15, 2024
概括
家庭因素显著预测青少年非自杀性自我伤害 (NSSI). 机器学习和回归模型在预测家庭层面的NSSI风险方面表现有希望,突出了家庭功能和冲突等关键预测因素.
科学领域:
- 青少年心理学 青少年心理学
- 公共卫生 公共卫生
- 机器学习在医疗保健中的应用.
背景情况:
- 青少年非自杀性自我伤害 (NSSI) 是一个关键的公共卫生问题,具有重要的家庭影响.
- 关于在家庭层面预测NSSI的研究有限,需要先进的预测方法.
- 机器学习 (ML) 技术为家庭级NSSI风险预测提供了与传统回归相比的潜在改进.
研究的目的:
- 通过逻辑回归和随机森林模型,评估NSSI在家庭层面的预测准确度.
- 确定与青少年NSSI相关的关键家庭相关预测因素和保护因素.
主要方法:
- 利用7967名学生及其主要照顾者的横截面数据集.
- 应用后勤回归和随机森林模型用于风险预测.
- 使用交叉验证指标评估模型性能,包括AUC,精度和Brier分数.
主要成果:
- 随机森林确定了家庭功能,家庭冲突和父母抑郁症作为顶级预测因素.
- 后勤回归突出了家庭精神疾病史 (OR:2.25) 的风险因素和父母寻求帮助 (OR:0.65) 的保护性因素.
- 模型性能显示物流回归的AUC为0.852,随机森林的AUC为0.835.
结论:
- 家庭相关因素对于预测青少年NSSI至关重要.
- 将传统的统计方法与ML集成,可以提高家庭层面的NSSI风险评估.
- 进一步的研究应该探索近距离和远距离预测因子,以进行全面的风险评估.
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